American Civil War experts recently posited that neither the tactical genius of Grant nor the moral superiority of Lincoln deserve credit for the Union victory.

In fact, they now believe the war was won because of something as mundane as the railroad. Between the years of 1861 and 1865 more railroad track was laid than during the previous combined history. The majority, by a 4-1 margin, was laid by the Union Army. This gave the Union a significant advantage in speed over the Confederates, enabling them to rapidly move troops, supplies and information from the distant manufacturing centers to battlegrounds much faster and efficiently than the geographically closer South.

Moral of the story? Having the assets, or knowledge, is one thing, but it’s the ability to deliver them when they are needed most that makes the difference.

As an industry, we’ve spent the past few years focused on data’s size – it is BIG data after all – and the individually addressable nature of digital channels. But big data is not enough ammunition in and of itself; it’s the ability to get that data to the front lines quickly and make it actionable that delivers a strategic advantage.

It’s becoming increasingly critical to harness data’s derived insight and deliver it to the point of an addressable interaction at the time it’s needed. In the year ahead, this “need for speed” will become the central data challenge for marketing organizations and technology companies that serve them.

Customer Expectations Redefine Effective Response Windows

The importance of speed, or timing, is not unknown in the marketing world. Going back to the roots of predictive analytics, “recency,” as a measure of time since last customer engagement, has long been held as the most predicative element of the classic RFM (recency, frequency, monetary) model. From practical experience we know that our likelihood of response drops dramatically as the period since last engagement lengthens.

What has changed in our omnichannel world is time’s unit of measure. The response window has shrunk from days or hours to minutes or even seconds. In the golden age of direct mail a catalog’s contributions would be measured in days, or even months. Today, a customer complains via Twitter and you’ve got minutes before the moment is lost to the fire hose. Mobile? Measured in seconds.

Not only has the response window shrunk, but so too has customer tolerance for a flawed experience. Back in 1950, as computers emerged in their modern form, a famous scientist by the name of Alan Turing developed a test, known as the “Turing Test,” for assessing a computer’s sophistication by gauging its ability to trick someone into believing it was human. This line of thinking applies very much to today’s customer expectation. Let’s call it the “Relevancy Test.” How long can a customer engage with your brand before a stutter or an obtuse comment negatively disrupts the experience?

Cross-Channel Interaction Complicates The Task

These interactions with the customer now happen at virtually any time or place. It’s not just a matter of using website browsing behavior to optimize the next item you show them. If the goal is engagement and relevance, what you show the customer next should be dictated by what they’ve bought in-store, expressed in their loyalty profile or tagged with your mobile app.

The demand on the marketing infrastructure has multiplied. Smart folks like Scott Brinker, who was one of the earliest advocate for the CMTO (chief marketing technology officer), or Laura McLellan, research VP at Gartner, who famously predicted marketing’s dramatic increase in technology spend, have offered frameworks for what these new marketing infrastructures will look like. To meet the requirements of the modern consumer, most marketing organizations need to ready themselves for a technology facelift, if not an overhaul.

Any infrastructure spend this upcoming year should be focused on how quickly it helps you move data from source to a point of application. In the multivariate and optimization spaces there are good examples of skinny tools that tap into specific source data points to improve a specific element of the customer experience (e.g., next best product). More broadly there are also tools that now enable marketers to speak with (not to) their customers – to create a consistent dialog across channels that reflects the entire brand (as opposed to siloed-channel) relationship. In either case, the emphasis and potential here is tied to the ability to monetize the value of the data.

Gauging Your Data Speed Readiness

While your future success and livelihood are inexorably tied to your ability to harness your data assets and apply them when needed, you need to assess where and how you can apply them today. An old cliché can be a simple litmus test for where to start:

Right customer: Can you easily segment and isolate individual customers to the point you can program around them?

Right place: Do you have an addressable ID for a customer in all relevant channels (e.g., physical address, email, mobile number, digital identifier, etc.)?

Right message: Can you dynamically alter messages to individual needs? Across all channels?

Right time: Can you respond seamlessly to a customer via any channel based upon an interaction in another?

If you can only answer yes to one of these, or maybe two, you likely should be focusing on a skinny point solution. Start small, with an application that taps into one or two offline data points, like warehouse inventory level and omnichannel rate of purchase, to drive next-best-product offers online. If you answered yes to two or more, then you can start exploring things dealing with multichannel attribution and understanding the most favored/likely cross-channel path to purchase.